During the past year, MEDx has been utilized extensively by NIH intramural researchers. The following is a summary of completed and ongoing projects - 1.Registration Engine/Print Engine Supported and enhanced the registration engine to optimally register Gad-enhanced T1-weighted images of brain tumor patients over time. This data is sent to the PACS for review by neuroradiologists. The registration engine also supports PET data as well as FLAIR MR. a. Developed Print Engine which automatically sends a one page print object to the filming station. This one page has been automatically formatted so that the referring physicians can easily show this film to patients to describe the progressive nature of the tumor. We estimate that this will save $100k annually in filming costs incurred by the NIH. b. Window/Level settings are automatically determined based on in-house developed algorithm. These values are encoded into the DICOM header. c. Improved Standard Uptake Value (SUV) calculation for PET images being registered. This involved discussions with Drs. Chen and Maass in the Nuclear Medicine department. d. Small bug fixes and minor features added during this time period. 2. MR Perfusion Analysis module The perfusion engine has been an ongoing project. Recent additions include: a. The compilation of the avw2dicom code on all platforms (required some heroic efforts). Similarly storescp and storescu have been compiled on all platforms making the perfusion engine platform independent i.e. it can run on Linux. RedHat, Sparc and MacIntel. b. There is now alongside the automated perfusion module an offline perfusion module capable of dicom send. c. The perfusion output maps have been standardized and are being registered on a routine basis to the reference image. d. Arterial input function and tissue plots, both in absolute and in relative percentage units, are also being sent as a separate series to the PACS. 3. Semi-Automated 4D Brain Lesion Segmentation a. Worked with Dr. Butman in DRD to develop a framework to automate the segmentation of brain tumors across multiple registered temporal MRI scans of a given patient. The purpose of this segmentation is to quantify the change in tumor volume over time during treatment by anti-angiogenic therapy. NCI is the institute performing these clinical drug trials. b. Only non-automated aspect of this framework is the required rough tracing (by the radiologist) of the tumor in the initial scan for each patient. All tumors in subsequent scans are automatically segmented. We incorporated tumor segmentation programs we previously developed into a batch mode which outputs the segmented scans as well as a output of tumor volume measures. 4. Plexiform Neurofibroma Lesion Segmentation a. Ongoing support of program which semi-automatically segments the plexiform neurofibrom lesions found in patients with NF1. b. This program is being used in multiple clinical trials held by NCI. In addition, sites outside of NIH have been using this technique we developed. 5. Automated MS Lesion Segmentation a. Worked with NINDS to design/develop an automated method to segment white matter lesions in patients with Multiple Sclerosis b. This method requires T2 and FLAIR images to properly segment lesions without including ventricles c. This program is also being used by the VA in Baltimore. 6. Multiple Sclerosis lesion analysis a. Supported the continued use of the MS lesion analysis program to track the progression of MS lesions over time. b. An all purpose graphics program was developed for the MS group for superimposing, formatting, and analyzing line profiles and histograms obtained from MS data. c. The AutoSpine program was much improved to enable the calculation of volumes within specified ROIs. The ROI specifications apart from polygon and freehand drawings, include options for bitmap addition and erasure. 8. T2 fitting module a. The T2 fitting module was further evaluated and developed. It now includes Marquardt Levenberg fitting and 3 point Regression. T2 histograms are currently being automatically generated. The suppression of tissue outside ROIs encompassing the brain, has been added to eliminate redundant fitting in order to speed up processing. b. A multiplot script has been developed in conjunction with the T2 fitting to allow the user to check the accuracy of the fits, retrospectively by having the fitted curve superimposed onto the original data 9. Fat-Spleen Module a. A module was prepared to allow the calculation of the liver to spleen ratio. The multiple ROIs used for the calculations are automatically placed for the user to adjust. The final ROIs used in the calculations are stamped onto the image prior to DICOM send to the PACS. b. The module has become part of routine protocols. 10. Hottest pixels module for PET arterial input function (AIF) determination a. A module was developed to allow automatic selection of a specified number of hottest pixels (see Mourik et al NeuroImage 39:1041-1050, 2008). The selected pixels are identified by markers on the volume to allow the user to check whether they indeed correspond to arterial pixels. b. In an alternate version, the specified number of hottest pixels is selected on each of the consecutive slices falling into a specified range. Iterative thresholding and statistics have been used in the implementation of both of these modules 11. Brain Connectivity Analysis a. Ongoing support and addition of new features to a MEDx module to assess brain connectivity based on correlation of the time course of all voxels to that of a seed voxel. b. The Automated Anatomic Labeling (AAL) atlas has been integrated into this module to automatically report anatomic structures showing functional connectivity with the brain region of interest. 12. Brown Fat Quantification A new MEDx program was developed to semi-automatically detect brown fat volume as seen in PET and CT images. This measurement was found to be necessary to describe a new finding for the bodys metabolic use of brown fat in patients studied in NIDDK. 13. Breast Screening Ongoing support and new features added to a MEDx derived program to measure breast density in mammograms. This program is semi-automated and requires the radiologist to adjust a threshold which differentiates adipose tissue and denser breast tissue. Visual feedback allows the radiologist to adjust this threshold on a case by case basis. NCI is using this method as part of an ongoing trial. 14. Data Server A new Xserve with approximately 16TERAbytes of RAID-based storage has been deployed. This system utilizes both ActiveDirectory and OpenIDrectory for authentication and access control to both personal and collaboratively share data. 15. Computational Server Several new RedHat Linux V5.x data servers have been deployed. All have multiple-processors (8/4/2) with memory ranging from 32Gb to 8Gb to 4Gb and local storage space ranging from 2Gb to 1GB to 0.5 Gb. These systems utlilize both ActiveDirectory an OpenDirectory for authentication and data access control. These systems are seamlessly integrated with both the Xserve-based data server and the CCs set of AD-based data servers. All are pre-loaded with numerous image processing packages such as MEDx, IDL,